Overview

Dataset statistics

Number of variables47
Number of observations25489
Missing cells221339
Missing cells (%)18.5%
Total size in memory9.1 MiB
Average record size in memory376.0 B

Variable types

Numeric19
Text28

Alerts

segm_comercial has 1685 (6.6%) missing valuesMissing
tipo_cli has 748 (2.9%) missing valuesMissing
sociedad_ccial_civ has 24553 (96.3%) missing valuesMissing
act_econom has 5427 (21.3%) missing valuesMissing
ciiu has 5457 (21.4%) missing valuesMissing
cod_ciiu has 1675 (6.6%) missing valuesMissing
riesgo_actividad_economica has 5457 (21.4%) missing valuesMissing
ocup has 3255 (12.8%) missing valuesMissing
riesgo_ocupacion has 3255 (12.8%) missing valuesMissing
cod_ocup has 3255 (12.8%) missing valuesMissing
ing_mes has 749 (2.9%) missing valuesMissing
egresos_mes has 749 (2.9%) missing valuesMissing
nombre_ciudad_dirp has 870 (3.4%) missing valuesMissing
cod_ciudad_dirp has 870 (3.4%) missing valuesMissing
riesgo_ciudad_de_residencia has 870 (3.4%) missing valuesMissing
pais_nacim has 2441 (9.6%) missing valuesMissing
cod_pais_nacim has 2441 (9.6%) missing valuesMissing
cv has 2441 (9.6%) missing valuesMissing
pais_origen_recursos has 4597 (18.0%) missing valuesMissing
pais_origen_recursos1 has 4597 (18.0%) missing valuesMissing
riesgo_pais_origen_de_recursos has 4597 (18.0%) missing valuesMissing
pais_residencia has 922 (3.6%) missing valuesMissing
pais_residencia1 has 922 (3.6%) missing valuesMissing
riesgo_pais_residencia has 922 (3.6%) missing valuesMissing
f_vinc has 748 (2.9%) missing valuesMissing
estado_cli has 818 (3.2%) missing valuesMissing
ctrl_terc has 748 (2.9%) missing valuesMissing
riesgo_cliente__ric_ has 5159 (20.2%) missing valuesMissing
f_ingreso_lc has 18730 (73.5%) missing valuesMissing
cod_categ_lc has 18730 (73.5%) missing valuesMissing
desc_categ has 18730 (73.5%) missing valuesMissing
cod_subcateg_lc has 18730 (73.5%) missing valuesMissing
desc_subcateg has 18730 (73.5%) missing valuesMissing
cod_nivel_cat has 18730 (73.5%) missing valuesMissing
motivo_ingreso_a_listas_de_control has 18730 (73.5%) missing valuesMissing
num_caso is highly skewed (γ1 = 159.5342584)Skewed
ing_mes is highly skewed (γ1 = 71.08869764)Skewed
egresos_mes is highly skewed (γ1 = 92.72176382)Skewed
monto_total_anual_transado__efectivo_ is highly skewed (γ1 = 105.6728519)Skewed
frecuencia_total_anual_transada__efectivo_ is highly skewed (γ1 = 158.969723)Skewed
monto_total_anual_transado__operaciones_internacionales_ is highly skewed (γ1 = 55.64027059)Skewed
frecuencia_total_anual_transada__operaciones_internacionales_ is highly skewed (γ1 = 159.6527479)Skewed
cod_ciiu has 3782 (14.8%) zerosZeros
ing_mes has 2462 (9.7%) zerosZeros
egresos_mes has 5749 (22.6%) zerosZeros
monto_total_anual_transado__efectivo_ has 12710 (49.9%) zerosZeros
frecuencia_total_anual_transada__efectivo_ has 12710 (49.9%) zerosZeros
monto_total_anual_transado__operaciones_internacionales_ has 17640 (69.2%) zerosZeros
frecuencia_total_anual_transada__operaciones_internacionales_ has 17640 (69.2%) zerosZeros

Reproduction

Analysis started2024-06-01 14:48:00.978445
Analysis finished2024-06-01 14:48:02.319244
Duration1.34 second
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

doc
Real number (ℝ)

Distinct18651
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.921521321 × 1014
Minimum-9.223097337 × 1018
Maximum9.223320127 × 1018
Zeros0
Zeros (%)0.0%
Negative12860
Negative (%)50.5%
Memory size199.3 KiB
2024-06-01T09:48:02.553479image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-9.223097337 × 1018
5-th percentile-8.248983799 × 1018
Q1-4.54767295 × 1018
median-7.510393039 × 1016
Q34.606820111 × 1018
95-th percentile8.296793255 × 1018
Maximum9.223320127 × 1018
Range-3.266098275 × 1014
Interquartile range (IQR)9.154493061 × 1018

Descriptive statistics

Standard deviation5.316798956 × 1018
Coefficient of variation (CV)-18198.73406
Kurtosis-1.197755738
Mean-2.921521321 × 1014
Median Absolute Deviation (MAD)4.582401089 × 1018
Skewness-0.003351034894
Sum-7.446665695 × 1018
Variance2.826835114 × 1037
MonotonicityNot monotonic
2024-06-01T09:48:02.803421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.34257383 × 101833
 
0.1%
-1.725293757 × 101832
 
0.1%
-5.279548882 × 101830
 
0.1%
-7.916166097 × 101830
 
0.1%
8.874921643 × 101827
 
0.1%
5.826077699 × 101826
 
0.1%
5.660908182 × 101826
 
0.1%
-3.879644911 × 101726
 
0.1%
-1.980603708 × 101825
 
0.1%
3.198690852 × 101824
 
0.1%
Other values (18641) 25210
98.9%
ValueCountFrequency (%)
-9.223097337 × 10181
< 0.1%
-9.220473409 × 10182
< 0.1%
-9.22024709 × 10181
< 0.1%
-9.218504903 × 10181
< 0.1%
-9.218330508 × 10182
< 0.1%
ValueCountFrequency (%)
9.223320127 × 10181
< 0.1%
9.222784968 × 10181
< 0.1%
9.221203706 × 10181
< 0.1%
9.219805152 × 10181
< 0.1%
9.219235204 × 10181
< 0.1%

cod_tipo_doc
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.247458965 × 1018
Minimum-7.857548262 × 1018
Maximum1.376715058 × 1018
Zeros0
Zeros (%)0.0%
Negative25232
Negative (%)99.0%
Memory size199.3 KiB
2024-06-01T09:48:03.963979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-7.857548262 × 1018
5-th percentile-7.857548262 × 1018
Q1-7.857548262 × 1018
median-7.857548262 × 1018
Q3-7.857548262 × 1018
95-th percentile-6.890108876 × 1016
Maximum1.376715058 × 1018
Range-9.212480754 × 1018
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.006437477 × 1018
Coefficient of variation (CV)-0.2768470283
Kurtosis7.770176888
Mean-7.247458965 × 1018
Median Absolute Deviation (MAD)0
Skewness3.07629462
Sum-4.786402423 × 1018
Variance4.02579135 × 1036
MonotonicityNot monotonic
2024-06-01T09:48:04.111975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
-7.857548262 × 101823277
91.3%
-6.890108876 × 10161021
 
4.0%
-2.081665374 × 1018905
 
3.6%
2.774543625 × 1017146
 
0.6%
1.376715058 × 101879
 
0.3%
1.186268725 × 101832
 
0.1%
-2.807093445 × 101818
 
0.1%
-1.161566327 × 101811
 
< 0.1%
ValueCountFrequency (%)
-7.857548262 × 101823277
91.3%
-2.807093445 × 101818
 
0.1%
-2.081665374 × 1018905
 
3.6%
-1.161566327 × 101811
 
< 0.1%
-6.890108876 × 10161021
 
4.0%
ValueCountFrequency (%)
1.376715058 × 101879
 
0.3%
1.186268725 × 101832
 
0.1%
2.774543625 × 1017146
 
0.6%
-6.890108876 × 10161021
4.0%
-1.161566327 × 101811
 
< 0.1%

nombre
Text

Distinct18445
Distinct (%)72.4%
Missing1
Missing (%)< 0.1%
Memory size199.3 KiB
2024-06-01T09:48:04.467031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length60
Median length50
Mean length26.32360326
Min length4

Characters and Unicode

Total characters670936
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15097 ?
Unique (%)59.2%

Sample

1st row3dabe5cb922a502fb0ad02a39ae2e1
2nd row3dabe5cb922a502fb0ad02a39ae2e1
3rd row75f601c0ac56dc6626dee95be07
4th row65df838b7cf4ac33308531fe1b5d
5th row27c86b9075
ValueCountFrequency (%)
14322f45a24529769457f887250 33
 
0.1%
6b7a35a79bd035c4c7e920b2a02cc01 33
 
0.1%
3373922fc5ef62ba731e4250846 30
 
0.1%
ade319b72b692ea2ea0eb967 30
 
0.1%
88e78e7951dfbd25fa9066 26
 
0.1%
1b8be170256f53188e74069c6af433 26
 
0.1%
2a46755edd6152e0aee435a0a 24
 
0.1%
a567664b1594f8ebb78de4df3d9fd 24
 
0.1%
ed1422650e0e609814a0c3211371a5c5e3 23
 
0.1%
6dbcaef29355953581adb8d3 23
 
0.1%
Other values (18435) 25216
98.9%
2024-06-01T09:48:05.194125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 42953
 
6.4%
7 42656
 
6.4%
b 42458
 
6.3%
5 42452
 
6.3%
6 42429
 
6.3%
a 42015
 
6.3%
9 41999
 
6.3%
c 41943
 
6.3%
f 41709
 
6.2%
2 41670
 
6.2%
Other values (6) 248652
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418249
62.3%
Lowercase Letter 252687
37.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 42656
10.2%
5 42452
10.1%
6 42429
10.1%
9 41999
10.0%
2 41670
10.0%
4 41518
9.9%
8 41516
9.9%
1 41482
9.9%
0 41389
9.9%
3 41138
9.8%
Lowercase Letter
ValueCountFrequency (%)
e 42953
17.0%
b 42458
16.8%
a 42015
16.6%
c 41943
16.6%
f 41709
16.5%
d 41609
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 418249
62.3%
Latin 252687
37.7%

Most frequent character per script

Common
ValueCountFrequency (%)
7 42656
10.2%
5 42452
10.1%
6 42429
10.1%
9 41999
10.0%
2 41670
10.0%
4 41518
9.9%
8 41516
9.9%
1 41482
9.9%
0 41389
9.9%
3 41138
9.8%
Latin
ValueCountFrequency (%)
e 42953
17.0%
b 42458
16.8%
a 42015
16.6%
c 41943
16.6%
f 41709
16.5%
d 41609
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 670936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 42953
 
6.4%
7 42656
 
6.4%
b 42458
 
6.3%
5 42452
 
6.3%
6 42429
 
6.3%
a 42015
 
6.3%
9 41999
 
6.3%
c 41943
 
6.3%
f 41709
 
6.2%
2 41670
 
6.2%
Other values (6) 248652
37.1%
Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:05.472173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length36
Median length20
Mean length20.75667935
Min length9

Characters and Unicode

Total characters529067
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowPosible Operacion LA
2nd rowPosible Operacion LA
3rd rowOperaciones intentadas
4th rowPosible Operacion LA
5th rowPosible Operacion LA
ValueCountFrequency (%)
posible 17318
24.9%
la 17318
24.9%
operacion 17318
24.9%
operaciones 7362
10.6%
intentadas 7361
10.6%
de 694
 
1.0%
recursos 618
 
0.9%
ilegal 618
 
0.9%
captacion 618
 
0.9%
activos 70
 
0.1%
Other values (25) 336
 
0.5%
2024-06-01T09:48:06.092698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 58667
11.1%
i 50752
 
9.6%
44142
 
8.3%
o 43516
 
8.2%
a 41422
 
7.8%
n 40104
 
7.6%
s 33361
 
6.3%
r 26062
 
4.9%
c 26060
 
4.9%
p 25357
 
4.8%
Other values (28) 139624
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 406172
76.8%
Uppercase Letter 78753
 
14.9%
Space Separator 44142
 
8.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 24813
31.5%
A 17488
22.2%
L 17451
22.2%
P 17323
22.0%
C 758
 
1.0%
I 178
 
0.2%
E 160
 
0.2%
T 127
 
0.2%
N 124
 
0.2%
S 100
 
0.1%
Other values (9) 231
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
e 58667
14.4%
i 50752
12.5%
o 43516
10.7%
a 41422
10.2%
n 40104
9.9%
s 33361
8.2%
r 26062
6.4%
c 26060
6.4%
p 25357
6.2%
l 18563
 
4.6%
Other values (8) 42308
10.4%
Space Separator
ValueCountFrequency (%)
44142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 484925
91.7%
Common 44142
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 58667
12.1%
i 50752
10.5%
o 43516
 
9.0%
a 41422
 
8.5%
n 40104
 
8.3%
s 33361
 
6.9%
r 26062
 
5.4%
c 26060
 
5.4%
p 25357
 
5.2%
O 24813
 
5.1%
Other values (27) 114811
23.7%
Common
ValueCountFrequency (%)
44142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 529067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 58667
11.1%
i 50752
 
9.6%
44142
 
8.3%
o 43516
 
8.2%
a 41422
 
7.8%
n 40104
 
7.6%
s 33361
 
6.3%
r 26062
 
4.9%
c 26060
 
4.9%
p 25357
 
4.8%
Other values (28) 139624
26.4%
Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:06.414402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length56
Median length20
Mean length26.193456
Min length10

Characters and Unicode

Total characters667645
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowPosible Operacion LA
2nd rowPosible Operacion LA
3rd rowCaptacion ilegal de recursos
4th rowFraccionamiento de operaciones o Pitufeo
5th rowPosible Operacion LA
ValueCountFrequency (%)
posible 13476
14.4%
operacion 13476
14.4%
la 13473
14.4%
de 11278
12.0%
ilegal 7375
7.9%
captacion 7374
7.9%
recursos 7374
7.9%
o 2917
 
3.1%
operaciones 2857
 
3.0%
fraccionamiento 2132
 
2.3%
Other values (58) 12035
12.8%
2024-06-01T09:48:06.948464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 68308
10.2%
68308
10.2%
o 64296
 
9.6%
i 62618
 
9.4%
a 49608
 
7.4%
c 42590
 
6.4%
s 39609
 
5.9%
r 38613
 
5.8%
n 34666
 
5.2%
l 30325
 
4.5%
Other values (33) 168704
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 530368
79.4%
Uppercase Letter 68969
 
10.3%
Space Separator 68308
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 68308
12.9%
o 64296
12.1%
i 62618
11.8%
a 49608
9.4%
c 42590
8.0%
s 39609
7.5%
r 38613
7.3%
n 34666
6.5%
l 30325
5.7%
p 25319
 
4.8%
Other values (13) 74416
14.0%
Uppercase Letter
ValueCountFrequency (%)
P 15593
22.6%
O 14287
20.7%
A 13672
19.8%
L 13526
19.6%
C 7498
10.9%
F 2144
 
3.1%
U 1544
 
2.2%
E 183
 
0.3%
I 173
 
0.3%
N 65
 
0.1%
Other values (9) 284
 
0.4%
Space Separator
ValueCountFrequency (%)
68308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 599337
89.8%
Common 68308
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 68308
11.4%
o 64296
10.7%
i 62618
10.4%
a 49608
 
8.3%
c 42590
 
7.1%
s 39609
 
6.6%
r 38613
 
6.4%
n 34666
 
5.8%
l 30325
 
5.1%
p 25319
 
4.2%
Other values (32) 143385
23.9%
Common
ValueCountFrequency (%)
68308
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 667645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 68308
10.2%
68308
10.2%
o 64296
 
9.6%
i 62618
 
9.4%
a 49608
 
7.4%
c 42590
 
6.4%
s 39609
 
5.9%
r 38613
 
5.8%
n 34666
 
5.2%
l 30325
 
4.5%
Other values (33) 168704
25.3%
Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:07.229654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length48
Median length40
Mean length27.96653458
Min length9

Characters and Unicode

Total characters712839
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowDelitos contra el sistema financiero
2nd rowDelitos contra el sistema financiero
3rd rowPosible Operacion LA
4th rowPosible Operacion LA
5th rowEnriquecimiento ilicito de particulares
ValueCountFrequency (%)
la 11608
12.4%
posible 11595
12.4%
operacion 11593
12.4%
delitos 7215
7.7%
contra 7215
7.7%
el 7200
7.7%
sistema 7200
7.7%
financiero 7200
7.7%
enriquecimiento 5228
5.6%
ilicito 5228
5.6%
Other values (45) 12237
13.1%
2024-06-01T09:48:07.925972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 82973
 
11.6%
68030
 
9.5%
e 65536
 
9.2%
o 52521
 
7.4%
n 40145
 
5.6%
a 39129
 
5.5%
c 38704
 
5.4%
r 38558
 
5.4%
s 34177
 
4.8%
l 32620
 
4.6%
Other values (33) 220446
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 522618
73.3%
Uppercase Letter 122191
 
17.1%
Space Separator 68030
 
9.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 82973
15.9%
e 65536
12.5%
o 52521
10.0%
n 40145
7.7%
a 39129
7.5%
c 38704
7.4%
r 38558
7.4%
s 34177
6.5%
l 32620
 
6.2%
t 32231
 
6.2%
Other values (13) 66024
12.6%
Uppercase Letter
ValueCountFrequency (%)
O 17948
14.7%
A 17410
14.2%
L 15515
12.7%
E 13549
11.1%
P 11616
9.5%
I 8358
6.8%
D 7250
5.9%
T 5995
 
4.9%
N 5990
 
4.9%
S 5924
 
4.8%
Other values (9) 12636
10.3%
Space Separator
ValueCountFrequency (%)
68030
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 644809
90.5%
Common 68030
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 82973
12.9%
e 65536
 
10.2%
o 52521
 
8.1%
n 40145
 
6.2%
a 39129
 
6.1%
c 38704
 
6.0%
r 38558
 
6.0%
s 34177
 
5.3%
l 32620
 
5.1%
t 32231
 
5.0%
Other values (32) 188215
29.2%
Common
ValueCountFrequency (%)
68030
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 712836
> 99.9%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 82973
 
11.6%
68030
 
9.5%
e 65536
 
9.2%
o 52521
 
7.4%
n 40145
 
5.6%
a 39129
 
5.5%
c 38704
 
5.4%
r 38558
 
5.4%
s 34177
 
4.8%
l 32620
 
4.6%
Other values (32) 220443
30.9%
None
ValueCountFrequency (%)
ó 3
100.0%

ros
Real number (ℝ)

Distinct1289
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.902839152 × 1017
Minimum-9.219533865 × 1018
Maximum9.223252571 × 1018
Zeros0
Zeros (%)0.0%
Negative11217
Negative (%)44.0%
Memory size199.3 KiB
2024-06-01T09:48:08.188925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-9.219533865 × 1018
5-th percentile-9.066073681 × 1018
Q1-4.428548911 × 1018
median1.696897692 × 1018
Q35.259446756 × 1018
95-th percentile7.939969191 × 1018
Maximum9.223252571 × 1018
Range-3.957638264 × 1015
Interquartile range (IQR)9.687995667 × 1018

Descriptive statistics

Standard deviation5.668831401 × 1018
Coefficient of variation (CV)11.56234424
Kurtosis-1.26997636
Mean4.902839152 × 1017
Median Absolute Deviation (MAD)3.578562619 × 1018
Skewness-0.4003669451
Sum8.400976325 × 1018
Variance3.213564945 × 1037
MonotonicityNot monotonic
2024-06-01T09:48:08.604606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.259446756 × 10186749
26.5%
-9.066073681 × 10181894
 
7.4%
-4.356937179 × 1018428
 
1.7%
-2.799660352 × 1018409
 
1.6%
-8.783737837 × 1018387
 
1.5%
-9.490156772 × 1016357
 
1.4%
-6.145176877 × 1018351
 
1.4%
-5.729196017 × 1018306
 
1.2%
5.275460311 × 1018306
 
1.2%
9.06514845 × 1017301
 
1.2%
Other values (1279) 14001
54.9%
ValueCountFrequency (%)
-9.219533865 × 10182
 
< 0.1%
-9.207327972 × 101886
0.3%
-9.187493332 × 10181
 
< 0.1%
-9.185249707 × 10181
 
< 0.1%
-9.166787808 × 10181
 
< 0.1%
ValueCountFrequency (%)
9.223252571 × 10181
 
< 0.1%
9.18190665 × 10181
 
< 0.1%
9.180759589 × 10186
< 0.1%
9.159243048 × 10181
 
< 0.1%
9.15749951 × 10181
 
< 0.1%

num_caso
Real number (ℝ)

SKEWED 

Distinct1282
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1023444.511
Minimum77887
Maximum901300693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:08.889211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum77887
5-th percentile844753
Q1912736
median927353
Q31041578
95-th percentile1251844
Maximum901300693
Range901222806
Interquartile range (IQR)128842

Descriptive statistics

Standard deviation5640588.208
Coefficient of variation (CV)5.511376677
Kurtosis25463.77725
Mean1023444.511
Median Absolute Deviation (MAD)61592
Skewness159.5342584
Sum2.608657714 × 1010
Variance3.181623534 × 1013
MonotonicityNot monotonic
2024-06-01T09:48:09.147081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
927353 6749
26.5%
844753 1894
 
7.4%
1041578 428
 
1.7%
852734 409
 
1.6%
1105528 387
 
1.5%
1174454 357
 
1.4%
1200828 351
 
1.4%
987131 306
 
1.2%
877832 306
 
1.2%
1016265 301
 
1.2%
Other values (1272) 14001
54.9%
ValueCountFrequency (%)
77887 2
< 0.1%
78166 1
< 0.1%
515956 1
< 0.1%
536892 2
< 0.1%
587206 1
< 0.1%
ValueCountFrequency (%)
901300693 1
 
< 0.1%
1296045 1
 
< 0.1%
1295883 10
< 0.1%
1293095 1
 
< 0.1%
1286066 16
0.1%

segm_comercial
Text

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing1685
Missing (%)6.6%
Memory size199.3 KiB
2024-06-01T09:48:09.409544image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length8.442152579
Min length4

Characters and Unicode

Total characters200957
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPERSONAL
2nd rowPERSONAL
3rd rowPERSONAL
4th rowPERSONAL
5th rowPLUS
ValueCountFrequency (%)
personal 11238
47.2%
independientes 5273
22.1%
plus 3263
 
13.7%
social 2203
 
9.3%
pymes 1564
 
6.6%
preferencial 236
 
1.0%
empresarial 17
 
0.1%
corporativa 7
 
< 0.1%
gobierno 3
 
< 0.1%
de 3
 
< 0.1%
2024-06-01T09:48:09.878788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 34645
17.2%
N 27296
13.6%
S 23558
11.7%
P 21598
10.7%
L 16957
8.4%
A 13725
 
6.8%
O 13461
 
6.7%
I 13012
 
6.5%
R 11764
 
5.9%
D 10552
 
5.3%
Other values (10) 14389
7.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200951
> 99.9%
Space Separator 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 34645
17.2%
N 27296
13.6%
S 23558
11.7%
P 21598
10.7%
L 16957
8.4%
A 13725
 
6.8%
O 13461
 
6.7%
I 13012
 
6.5%
R 11764
 
5.9%
D 10552
 
5.3%
Other values (9) 14383
7.2%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 200951
> 99.9%
Common 6
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 34645
17.2%
N 27296
13.6%
S 23558
11.7%
P 21598
10.7%
L 16957
8.4%
A 13725
 
6.8%
O 13461
 
6.7%
I 13012
 
6.5%
R 11764
 
5.9%
D 10552
 
5.3%
Other values (9) 14383
7.2%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 34645
17.2%
N 27296
13.6%
S 23558
11.7%
P 21598
10.7%
L 16957
8.4%
A 13725
 
6.8%
O 13461
 
6.7%
I 13012
 
6.5%
R 11764
 
5.9%
D 10552
 
5.3%
Other values (10) 14389
7.2%

tipo_cli
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing748
Missing (%)2.9%
Memory size199.3 KiB
2024-06-01T09:48:10.095781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length16
Median length15
Mean length15.04106544
Min length15

Characters and Unicode

Total characters372131
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPERSONA NATURAL
2nd rowPERSONA NATURAL
3rd rowPERSONA NATURAL
4th rowPERSONA NATURAL
5th rowPERSONA NATURAL
ValueCountFrequency (%)
persona 24741
50.0%
natural 23725
47.9%
jurídica 1016
 
2.1%
2024-06-01T09:48:10.512496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 73207
19.7%
R 49482
13.3%
N 48466
13.0%
P 24741
 
6.6%
E 24741
 
6.6%
S 24741
 
6.6%
O 24741
 
6.6%
24741
 
6.6%
U 24741
 
6.6%
T 23725
 
6.4%
Other values (6) 28805
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 346374
93.1%
Space Separator 24741
 
6.6%
Lowercase Letter 1016
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 73207
21.1%
R 49482
14.3%
N 48466
14.0%
P 24741
 
7.1%
E 24741
 
7.1%
S 24741
 
7.1%
O 24741
 
7.1%
U 24741
 
7.1%
T 23725
 
6.8%
L 23725
 
6.8%
Other values (4) 4064
 
1.2%
Space Separator
ValueCountFrequency (%)
24741
100.0%
Lowercase Letter
ValueCountFrequency (%)
í 1016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 347390
93.4%
Common 24741
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 73207
21.1%
R 49482
14.2%
N 48466
14.0%
P 24741
 
7.1%
E 24741
 
7.1%
S 24741
 
7.1%
O 24741
 
7.1%
U 24741
 
7.1%
T 23725
 
6.8%
L 23725
 
6.8%
Other values (5) 5080
 
1.5%
Common
ValueCountFrequency (%)
24741
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 371115
99.7%
None 1016
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 73207
19.7%
R 49482
13.3%
N 48466
13.1%
P 24741
 
6.7%
E 24741
 
6.7%
S 24741
 
6.7%
O 24741
 
6.7%
24741
 
6.7%
U 24741
 
6.7%
T 23725
 
6.4%
Other values (5) 27789
 
7.5%
None
ValueCountFrequency (%)
í 1016
100.0%

sociedad_ccial_civ
Text

MISSING 

Distinct11
Distinct (%)1.2%
Missing24553
Missing (%)96.3%
Memory size199.3 KiB
2024-06-01T09:48:10.791389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length19
Median length18
Mean length17.47649573
Min length9

Characters and Unicode

Total characters16358
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowS. ANON. SIMP. SAS
2nd rowS. ANON. SIMP. SAS
3rd rowS. ANON. SIMP. SAS
4th rowNO APLICA
5th rowNO APLICA
ValueCountFrequency (%)
s 791
22.2%
anon 783
21.9%
simp 783
21.9%
sas 783
21.9%
no 45
 
1.3%
aplica 45
 
1.3%
sociedad 43
 
1.2%
ent 39
 
1.1%
sin 39
 
1.1%
animo 39
 
1.1%
Other values (16) 179
 
5.0%
2024-06-01T09:48:11.336702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 3248
19.9%
2633
16.1%
. 2404
14.7%
A 1852
11.3%
N 1825
11.2%
I 1032
 
6.3%
O 971
 
5.9%
M 888
 
5.4%
P 855
 
5.2%
E 162
 
1.0%
Other values (13) 488
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11281
69.0%
Space Separator 2633
 
16.1%
Other Punctuation 2404
 
14.7%
Lowercase Letter 40
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 3248
28.8%
A 1852
16.4%
N 1825
16.2%
I 1032
 
9.1%
O 971
 
8.6%
M 888
 
7.9%
P 855
 
7.6%
E 162
 
1.4%
D 135
 
1.2%
C 116
 
1.0%
Other values (5) 197
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
o 15
37.5%
n 5
 
12.5%
s 5
 
12.5%
r 5
 
12.5%
c 5
 
12.5%
i 5
 
12.5%
Space Separator
ValueCountFrequency (%)
2633
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11321
69.2%
Common 5037
30.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 3248
28.7%
A 1852
16.4%
N 1825
16.1%
I 1032
 
9.1%
O 971
 
8.6%
M 888
 
7.8%
P 855
 
7.6%
E 162
 
1.4%
D 135
 
1.2%
C 116
 
1.0%
Other values (11) 237
 
2.1%
Common
ValueCountFrequency (%)
2633
52.3%
. 2404
47.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 3248
19.9%
2633
16.1%
. 2404
14.7%
A 1852
11.3%
N 1825
11.2%
I 1032
 
6.3%
O 971
 
5.9%
M 888
 
5.4%
P 855
 
5.2%
E 162
 
1.0%
Other values (13) 488
 
3.0%

act_econom
Text

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing5427
Missing (%)21.3%
Memory size199.3 KiB
2024-06-01T09:48:11.688278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length40
Median length10
Mean length16.19813578
Min length9

Characters and Unicode

Total characters324967
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASALARIADO
2nd rowASALARIADO
3rd rowASALARIADO
4th rowASALARIADO
5th rowASALARIADO
ValueCountFrequency (%)
asalariado 11987
29.7%
o 3768
 
9.4%
prestar 3768
 
9.4%
servicios 3768
 
9.4%
suministrar 3768
 
9.4%
vender 2316
 
5.7%
y/o 2316
 
5.7%
comprar 2316
 
5.7%
y 980
 
2.4%
rentista 600
 
1.5%
Other values (14) 4707
 
11.7%
2024-06-01T09:48:12.269449image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 64512
19.9%
R 44674
13.7%
S 32896
10.1%
I 29981
9.2%
O 25702
 
7.9%
20232
 
6.2%
D 14903
 
4.6%
E 14245
 
4.4%
L 13308
 
4.1%
T 11298
 
3.5%
Other values (13) 53216
16.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 301439
92.8%
Space Separator 20232
 
6.2%
Other Punctuation 3296
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 64512
21.4%
R 44674
14.8%
S 32896
10.9%
I 29981
9.9%
O 25702
 
8.5%
D 14903
 
4.9%
E 14245
 
4.7%
L 13308
 
4.4%
T 11298
 
3.7%
C 9717
 
3.2%
Other values (10) 40203
13.3%
Other Punctuation
ValueCountFrequency (%)
/ 2316
70.3%
, 980
29.7%
Space Separator
ValueCountFrequency (%)
20232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 301439
92.8%
Common 23528
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 64512
21.4%
R 44674
14.8%
S 32896
10.9%
I 29981
9.9%
O 25702
 
8.5%
D 14903
 
4.9%
E 14245
 
4.7%
L 13308
 
4.4%
T 11298
 
3.7%
C 9717
 
3.2%
Other values (10) 40203
13.3%
Common
ValueCountFrequency (%)
20232
86.0%
/ 2316
 
9.8%
, 980
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 324967
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 64512
19.9%
R 44674
13.7%
S 32896
10.1%
I 29981
9.2%
O 25702
 
7.9%
20232
 
6.2%
D 14903
 
4.6%
E 14245
 
4.4%
L 13308
 
4.1%
T 11298
 
3.5%
Other values (13) 53216
16.4%

ciiu
Text

MISSING 

Distinct333
Distinct (%)1.7%
Missing5457
Missing (%)21.4%
Memory size199.3 KiB
2024-06-01T09:48:12.931579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length174
Median length11
Mean length31.51941893
Min length10

Characters and Unicode

Total characters631397
Distinct characters38
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)0.3%

Sample

1st rowASALARIADOS
2nd rowASALARIADOS
3rd rowASALARIADOS
4th rowASALARIADOS
5th rowASALARIADOS
ValueCountFrequency (%)
asalariados 11957
 
14.3%
de 11478
 
13.7%
y 3187
 
3.8%
actividades 2902
 
3.5%
por 2639
 
3.2%
comercio 2309
 
2.8%
al 2224
 
2.7%
otras 1580
 
1.9%
menor 1545
 
1.8%
n.c.p 1434
 
1.7%
Other values (666) 42417
50.7%
2024-06-01T09:48:13.868858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 94677
15.0%
63640
10.1%
S 61057
9.7%
O 51369
8.1%
E 50874
8.1%
I 48185
 
7.6%
R 43259
 
6.9%
D 37992
 
6.0%
L 28574
 
4.5%
C 28539
 
4.5%
Other values (28) 123231
19.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 559000
88.5%
Space Separator 63640
 
10.1%
Other Punctuation 7364
 
1.2%
Open Punctuation 696
 
0.1%
Close Punctuation 696
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 94677
16.9%
S 61057
10.9%
O 51369
9.2%
E 50874
9.1%
I 48185
8.6%
R 43259
7.7%
D 37992
6.8%
L 28574
 
5.1%
C 28539
 
5.1%
T 23485
 
4.2%
Other values (21) 90989
16.3%
Other Punctuation
ValueCountFrequency (%)
. 4578
62.2%
, 2707
36.8%
; 79
 
1.1%
Space Separator
ValueCountFrequency (%)
63640
100.0%
Open Punctuation
ValueCountFrequency (%)
( 696
100.0%
Close Punctuation
ValueCountFrequency (%)
) 696
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 559000
88.5%
Common 72397
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 94677
16.9%
S 61057
10.9%
O 51369
9.2%
E 50874
9.1%
I 48185
8.6%
R 43259
7.7%
D 37992
6.8%
L 28574
 
5.1%
C 28539
 
5.1%
T 23485
 
4.2%
Other values (21) 90989
16.3%
Common
ValueCountFrequency (%)
63640
87.9%
. 4578
 
6.3%
, 2707
 
3.7%
( 696
 
1.0%
) 696
 
1.0%
; 79
 
0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 623729
98.8%
None 7668
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 94677
15.2%
63640
10.2%
S 61057
9.8%
O 51369
8.2%
E 50874
8.2%
I 48185
7.7%
R 43259
 
6.9%
D 37992
 
6.1%
L 28574
 
4.6%
C 28539
 
4.6%
Other values (22) 115563
18.5%
None
ValueCountFrequency (%)
Ó 2974
38.8%
Í 2674
34.9%
É 961
 
12.5%
Á 827
 
10.8%
Ñ 215
 
2.8%
Ú 17
 
0.2%

cod_ciiu
Real number (ℝ)

MISSING  ZEROS 

Distinct334
Distinct (%)1.4%
Missing1675
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean1758.550349
Minimum0
Maximum9900
Zeros3782
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:14.181285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median10
Q34530
95-th percentile8299
Maximum9900
Range9900
Interquartile range (IQR)4520

Descriptive statistics

Standard deviation2929.629259
Coefficient of variation (CV)1.665934252
Kurtosis0.3144259927
Mean1758.550349
Median Absolute Deviation (MAD)0
Skewness1.345631023
Sum41878118
Variance8582727.597
MonotonicityNot monotonic
2024-06-01T09:48:14.446846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 11957
46.9%
0 3782
 
14.8%
8299 803
 
3.2%
90 600
 
2.4%
9609 248
 
1.0%
4771 233
 
0.9%
4791 219
 
0.9%
7490 183
 
0.7%
9602 160
 
0.6%
4799 158
 
0.6%
Other values (324) 5471
21.5%
(Missing) 1675
 
6.6%
ValueCountFrequency (%)
0 3782
 
14.8%
10 11957
46.9%
90 600
 
2.4%
111 8
 
< 0.1%
112 17
 
0.1%
ValueCountFrequency (%)
9900 7
 
< 0.1%
9820 8
 
< 0.1%
9810 6
 
< 0.1%
9700 11
 
< 0.1%
9609 248
1.0%
Distinct3
Distinct (%)< 0.1%
Missing5457
Missing (%)21.4%
Memory size199.3 KiB
2024-06-01T09:48:14.603092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.195686901
Min length4

Characters and Unicode

Total characters84048
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBAJO
2nd rowBAJO
3rd rowBAJO
4th rowBAJO
5th rowBAJO
ValueCountFrequency (%)
bajo 15859
79.2%
medio 3920
 
19.6%
alto 253
 
1.3%
2024-06-01T09:48:14.946758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 20032
23.8%
A 16112
19.2%
B 15859
18.9%
J 15859
18.9%
M 3920
 
4.7%
E 3920
 
4.7%
D 3920
 
4.7%
I 3920
 
4.7%
L 253
 
0.3%
T 253
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 84048
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 20032
23.8%
A 16112
19.2%
B 15859
18.9%
J 15859
18.9%
M 3920
 
4.7%
E 3920
 
4.7%
D 3920
 
4.7%
I 3920
 
4.7%
L 253
 
0.3%
T 253
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 84048
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 20032
23.8%
A 16112
19.2%
B 15859
18.9%
J 15859
18.9%
M 3920
 
4.7%
E 3920
 
4.7%
D 3920
 
4.7%
I 3920
 
4.7%
L 253
 
0.3%
T 253
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 20032
23.8%
A 16112
19.2%
B 15859
18.9%
J 15859
18.9%
M 3920
 
4.7%
E 3920
 
4.7%
D 3920
 
4.7%
I 3920
 
4.7%
L 253
 
0.3%
T 253
 
0.3%

ocup
Text

MISSING 

Distinct14
Distinct (%)0.1%
Missing3255
Missing (%)12.8%
Memory size199.3 KiB
2024-06-01T09:48:15.181689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length25
Median length24
Mean length11.79783215
Min length4

Characters and Unicode

Total characters262313
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEMPLEADO
2nd rowEMPLEADO
3rd rowEMPLEADO
4th rowDESEMPLEADO CON INGRESOS
5th rowPROFESIONAL INDEPENDIENTE
ValueCountFrequency (%)
empleado 9499
31.1%
independiente 6166
20.2%
desempleado 1742
 
5.7%
ingresos 1742
 
5.7%
con 1665
 
5.5%
estudiante 1296
 
4.2%
comerciante 1273
 
4.2%
de 1128
 
3.7%
ama 918
 
3.0%
casa 918
 
3.0%
Other values (11) 4166
13.7%
2024-06-01T09:48:15.597407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 58284
22.2%
D 28202
10.8%
N 26293
10.0%
O 21115
 
8.0%
A 20066
 
7.6%
I 19179
 
7.3%
P 18686
 
7.1%
M 13432
 
5.1%
L 12224
 
4.7%
T 11328
 
4.3%
Other values (8) 33504
12.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 253578
96.7%
Space Separator 8279
 
3.2%
Dash Punctuation 456
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 58284
23.0%
D 28202
11.1%
N 26293
10.4%
O 21115
 
8.3%
A 20066
 
7.9%
I 19179
 
7.6%
P 18686
 
7.4%
M 13432
 
5.3%
L 12224
 
4.8%
T 11328
 
4.5%
Other values (6) 24769
9.8%
Space Separator
ValueCountFrequency (%)
8279
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 253578
96.7%
Common 8735
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 58284
23.0%
D 28202
11.1%
N 26293
10.4%
O 21115
 
8.3%
A 20066
 
7.9%
I 19179
 
7.6%
P 18686
 
7.4%
M 13432
 
5.3%
L 12224
 
4.8%
T 11328
 
4.5%
Other values (6) 24769
9.8%
Common
ValueCountFrequency (%)
8279
94.8%
- 456
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 58284
22.2%
D 28202
10.8%
N 26293
10.0%
O 21115
 
8.0%
A 20066
 
7.6%
I 19179
 
7.3%
P 18686
 
7.1%
M 13432
 
5.1%
L 12224
 
4.7%
T 11328
 
4.3%
Other values (8) 33504
12.8%

riesgo_ocupacion
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing3255
Missing (%)12.8%
Memory size199.3 KiB
2024-06-01T09:48:15.784863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.626607898
Min length4

Characters and Unicode

Total characters102868
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedio
2nd rowMedio
3rd rowMedio
4th rowBajo
5th rowMedio
ValueCountFrequency (%)
medio 13932
62.7%
alto 5661
25.5%
bajo 2641
 
11.9%
2024-06-01T09:48:16.128871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 22234
21.6%
M 13932
13.5%
e 13932
13.5%
d 13932
13.5%
i 13932
13.5%
A 5661
 
5.5%
l 5661
 
5.5%
t 5661
 
5.5%
B 2641
 
2.6%
a 2641
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80634
78.4%
Uppercase Letter 22234
 
21.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 22234
27.6%
e 13932
17.3%
d 13932
17.3%
i 13932
17.3%
l 5661
 
7.0%
t 5661
 
7.0%
a 2641
 
3.3%
j 2641
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
M 13932
62.7%
A 5661
25.5%
B 2641
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 102868
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 22234
21.6%
M 13932
13.5%
e 13932
13.5%
d 13932
13.5%
i 13932
13.5%
A 5661
 
5.5%
l 5661
 
5.5%
t 5661
 
5.5%
B 2641
 
2.6%
a 2641
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 22234
21.6%
M 13932
13.5%
e 13932
13.5%
d 13932
13.5%
i 13932
13.5%
A 5661
 
5.5%
l 5661
 
5.5%
t 5661
 
5.5%
B 2641
 
2.6%
a 2641
 
2.6%

cod_ocup
Text

MISSING 

Distinct14
Distinct (%)0.1%
Missing3255
Missing (%)12.8%
Memory size199.3 KiB
2024-06-01T09:48:16.269464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters22234
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th rowI
5th rowP
ValueCountFrequency (%)
1 9043
40.7%
3 5451
24.5%
i 1665
 
7.5%
2 1296
 
5.8%
8 1273
 
5.7%
4 918
 
4.1%
p 715
 
3.2%
o 609
 
2.7%
e 456
 
2.1%
5 354
 
1.6%
Other values (4) 454
 
2.0%
2024-06-01T09:48:16.566296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9043
40.7%
3 5451
24.5%
I 1665
 
7.5%
2 1296
 
5.8%
8 1273
 
5.7%
4 918
 
4.1%
P 715
 
3.2%
O 609
 
2.7%
E 456
 
2.1%
5 354
 
1.6%
Other values (4) 454
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18712
84.2%
Uppercase Letter 3522
 
15.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9043
48.3%
3 5451
29.1%
2 1296
 
6.9%
8 1273
 
6.8%
4 918
 
4.9%
5 354
 
1.9%
9 210
 
1.1%
7 109
 
0.6%
6 58
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
I 1665
47.3%
P 715
20.3%
O 609
 
17.3%
E 456
 
12.9%
S 77
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 18712
84.2%
Latin 3522
 
15.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9043
48.3%
3 5451
29.1%
2 1296
 
6.9%
8 1273
 
6.8%
4 918
 
4.9%
5 354
 
1.9%
9 210
 
1.1%
7 109
 
0.6%
6 58
 
0.3%
Latin
ValueCountFrequency (%)
I 1665
47.3%
P 715
20.3%
O 609
 
17.3%
E 456
 
12.9%
S 77
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9043
40.7%
3 5451
24.5%
I 1665
 
7.5%
2 1296
 
5.8%
8 1273
 
5.7%
4 918
 
4.1%
P 715
 
3.2%
O 609
 
2.7%
E 456
 
2.1%
5 354
 
1.6%
Other values (4) 454
 
2.0%

ing_mes
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct8327
Distinct (%)33.7%
Missing749
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean74013245.73
Minimum0
Maximum2.215158686 × 1011
Zeros2462
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:16.784999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000000
median1940544
Q35000554
95-th percentile34653204.6
Maximum2.215158686 × 1011
Range2.215158686 × 1011
Interquartile range (IQR)4000554

Descriptive statistics

Standard deviation2481076314
Coefficient of variation (CV)33.52205797
Kurtosis5727.03509
Mean74013245.73
Median Absolute Deviation (MAD)1296335
Skewness71.08869764
Sum1.831087699 × 1012
Variance6.155739675 × 1018
MonotonicityNot monotonic
2024-06-01T09:48:16.988079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2462
 
9.7%
3000000 791
 
3.1%
1000000 685
 
2.7%
1 633
 
2.5%
2000000 576
 
2.3%
1500000 525
 
2.1%
5000000 335
 
1.3%
1200000 280
 
1.1%
4000000 219
 
0.9%
2500000 208
 
0.8%
Other values (8317) 18026
70.7%
(Missing) 749
 
2.9%
ValueCountFrequency (%)
0 2462
9.7%
1 633
 
2.5%
5 1
 
< 0.1%
10 1
 
< 0.1%
100 3
 
< 0.1%
ValueCountFrequency (%)
2.215158686 × 10112
< 0.1%
1.321382328 × 10111
< 0.1%
9.7868117 × 10102
< 0.1%
9.029108408 × 10101
< 0.1%
4.566887658 × 10101
< 0.1%

egresos_mes
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct959
Distinct (%)3.9%
Missing749
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean44670045.54
Minimum0
Maximum2.122375538 × 1011
Zeros5749
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:17.206777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150000
median500000
Q31500000
95-th percentile9000000
Maximum2.122375538 × 1011
Range2.122375538 × 1011
Interquartile range (IQR)1450000

Descriptive statistics

Standard deviation2057112621
Coefficient of variation (CV)46.05127655
Kurtosis9290.720925
Mean44670045.54
Median Absolute Deviation (MAD)500000
Skewness92.72176382
Sum1.105136927 × 1012
Variance4.231712334 × 1018
MonotonicityNot monotonic
2024-06-01T09:48:17.409847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5749
22.6%
2000000 2205
 
8.7%
500000 1883
 
7.4%
1000000 1464
 
5.7%
200000 1152
 
4.5%
300000 987
 
3.9%
100000 832
 
3.3%
600000 823
 
3.2%
800000 810
 
3.2%
400000 738
 
2.9%
Other values (949) 8097
31.8%
(Missing) 749
 
2.9%
ValueCountFrequency (%)
0 5749
22.6%
1 32
 
0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
2.122375538 × 10112
< 0.1%
8.2276407 × 10101
< 0.1%
6.153963669 × 10101
< 0.1%
3.030499633 × 10101
< 0.1%
2.286770352 × 10101
< 0.1%

nombre_ciudad_dirp
Text

MISSING 

Distinct561
Distinct (%)2.3%
Missing870
Missing (%)3.4%
Memory size199.3 KiB
2024-06-01T09:48:17.757053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length31
Median length20
Mean length9.380965921
Min length3

Characters and Unicode

Total characters230950
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)0.7%

Sample

1st rowdf081d2
2nd rowdf081d2
3rd rowb9a6a2fbf54
4th row462ec81fa2acc
5th rowb9a6a2fbf54
ValueCountFrequency (%)
b9a6a2fbf54 2995
 
12.2%
7bb20f00 2774
 
11.3%
14d054 2658
 
10.8%
99a151c28cdeee8 2053
 
8.3%
462ec81fa2acc 1945
 
7.9%
4caa 1201
 
4.9%
5eebeed4ec18 852
 
3.5%
2e0810 586
 
2.4%
2d1e0014b6a 463
 
1.9%
7c72fb2 363
 
1.5%
Other values (551) 8729
35.5%
2024-06-01T09:48:18.314123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 19218
 
8.3%
b 18639
 
8.1%
c 18569
 
8.0%
2 18530
 
8.0%
e 17686
 
7.7%
0 17531
 
7.6%
4 17157
 
7.4%
1 16499
 
7.1%
f 14539
 
6.3%
8 13965
 
6.0%
Other values (6) 58617
25.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132349
57.3%
Lowercase Letter 98601
42.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18530
14.0%
0 17531
13.2%
4 17157
13.0%
1 16499
12.5%
8 13965
10.6%
5 13102
9.9%
9 11515
8.7%
6 11443
8.6%
7 8633
6.5%
3 3974
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
a 19218
19.5%
b 18639
18.9%
c 18569
18.8%
e 17686
17.9%
f 14539
14.7%
d 9950
10.1%

Most occurring scripts

ValueCountFrequency (%)
Common 132349
57.3%
Latin 98601
42.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18530
14.0%
0 17531
13.2%
4 17157
13.0%
1 16499
12.5%
8 13965
10.6%
5 13102
9.9%
9 11515
8.7%
6 11443
8.6%
7 8633
6.5%
3 3974
 
3.0%
Latin
ValueCountFrequency (%)
a 19218
19.5%
b 18639
18.9%
c 18569
18.8%
e 17686
17.9%
f 14539
14.7%
d 9950
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 19218
 
8.3%
b 18639
 
8.1%
c 18569
 
8.0%
2 18530
 
8.0%
e 17686
 
7.7%
0 17531
 
7.6%
4 17157
 
7.4%
1 16499
 
7.1%
f 14539
 
6.3%
8 13965
 
6.0%
Other values (6) 58617
25.4%

cod_ciudad_dirp
Real number (ℝ)

MISSING 

Distinct579
Distinct (%)2.4%
Missing870
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean-4.496140242 × 1016
Minimum-9.218290829 × 1018
Maximum9.17782144 × 1018
Zeros0
Zeros (%)0.0%
Negative11855
Negative (%)46.5%
Memory size199.3 KiB
2024-06-01T09:48:18.548449image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-9.218290829 × 1018
5-th percentile-7.857548262 × 1018
Q1-4.698414405 × 1018
median8.916107089 × 1016
Q33.629450273 × 1018
95-th percentile7.041763508 × 1018
Maximum9.17782144 × 1018
Range1.839611227 × 1019
Interquartile range (IQR)8.327864678 × 1018

Descriptive statistics

Standard deviation4.90807172 × 1018
Coefficient of variation (CV)-109.1618912
Kurtosis-1.015919003
Mean-4.496140242 × 1016
Median Absolute Deviation (MAD)3.714224782 × 1018
Skewness-0.04185180248
Sum-1.106904766 × 1021
Variance2.408916801 × 1037
MonotonicityNot monotonic
2024-06-01T09:48:18.782735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.772136271 × 10182995
 
11.8%
8.916107089 × 10162774
 
10.9%
-5.38323629 × 10172658
 
10.4%
-7.857548262 × 10182053
 
8.1%
-5.120230582 × 10181945
 
7.6%
3.616617957 × 10181201
 
4.7%
2.615762407 × 1018852
 
3.3%
3.629450273 × 1018586
 
2.3%
-3.625063711 × 1018463
 
1.8%
8.747076692 × 1018363
 
1.4%
Other values (569) 8729
34.2%
(Missing) 870
 
3.4%
ValueCountFrequency (%)
-9.218290829 × 10181
 
< 0.1%
-9.186792896 × 10181
 
< 0.1%
-9.180286407 × 10181
 
< 0.1%
-9.166496858 × 10181
 
< 0.1%
-9.156710756 × 101813
0.1%
ValueCountFrequency (%)
9.17782144 × 101810
< 0.1%
9.172480467 × 10188
 
< 0.1%
9.06401736 × 10181
 
< 0.1%
9.061552933 × 101822
0.1%
9.030964331 × 10184
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing870
Missing (%)3.4%
Memory size199.3 KiB
2024-06-01T09:48:18.954870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.192859174
Min length4

Characters and Unicode

Total characters103224
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMEDIO
2nd rowMEDIO
3rd rowALTO
4th rowALTO
5th rowALTO
ValueCountFrequency (%)
alto 16753
68.0%
medio 4748
 
19.3%
bajo 3118
 
12.7%
2024-06-01T09:48:19.282917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 24619
23.9%
A 19871
19.3%
L 16753
16.2%
T 16753
16.2%
M 4748
 
4.6%
E 4748
 
4.6%
D 4748
 
4.6%
I 4748
 
4.6%
B 3118
 
3.0%
J 3118
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 103224
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 24619
23.9%
A 19871
19.3%
L 16753
16.2%
T 16753
16.2%
M 4748
 
4.6%
E 4748
 
4.6%
D 4748
 
4.6%
I 4748
 
4.6%
B 3118
 
3.0%
J 3118
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 103224
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 24619
23.9%
A 19871
19.3%
L 16753
16.2%
T 16753
16.2%
M 4748
 
4.6%
E 4748
 
4.6%
D 4748
 
4.6%
I 4748
 
4.6%
B 3118
 
3.0%
J 3118
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 24619
23.9%
A 19871
19.3%
L 16753
16.2%
T 16753
16.2%
M 4748
 
4.6%
E 4748
 
4.6%
D 4748
 
4.6%
I 4748
 
4.6%
B 3118
 
3.0%
J 3118
 
3.0%

pais_nacim
Text

MISSING 

Distinct33
Distinct (%)0.1%
Missing2441
Missing (%)9.6%
Memory size199.3 KiB
2024-06-01T09:48:19.501609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length15
Median length8
Mean length8.090593544
Min length4

Characters and Unicode

Total characters186472
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowARGENTINA
ValueCountFrequency (%)
colombia 21246
91.7%
venezuela 1617
 
7.0%
estados 73
 
0.3%
unidos 73
 
0.3%
i 17
 
0.1%
virgenes 15
 
0.1%
eeuu 15
 
0.1%
francia 10
 
< 0.1%
ecuador 10
 
< 0.1%
argentina 9
 
< 0.1%
Other values (31) 80
 
0.3%
2024-06-01T09:48:19.923385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 42679
22.9%
A 23087
12.4%
L 22883
12.3%
I 21411
11.5%
C 21283
11.4%
M 21263
11.4%
B 21250
11.4%
E 5039
 
2.7%
N 1774
 
1.0%
U 1760
 
0.9%
Other values (15) 4043
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 186355
99.9%
Space Separator 117
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 42679
22.9%
A 23087
12.4%
L 22883
12.3%
I 21411
11.5%
C 21283
11.4%
M 21263
11.4%
B 21250
11.4%
E 5039
 
2.7%
N 1774
 
1.0%
U 1760
 
0.9%
Other values (14) 3926
 
2.1%
Space Separator
ValueCountFrequency (%)
117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 186355
99.9%
Common 117
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 42679
22.9%
A 23087
12.4%
L 22883
12.3%
I 21411
11.5%
C 21283
11.4%
M 21263
11.4%
B 21250
11.4%
E 5039
 
2.7%
N 1774
 
1.0%
U 1760
 
0.9%
Other values (14) 3926
 
2.1%
Common
ValueCountFrequency (%)
117
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186467
> 99.9%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 42679
22.9%
A 23087
12.4%
L 22883
12.3%
I 21411
11.5%
C 21283
11.4%
M 21263
11.4%
B 21250
11.4%
E 5039
 
2.7%
N 1774
 
1.0%
U 1760
 
0.9%
Other values (14) 4038
 
2.2%
None
ValueCountFrequency (%)
Ñ 5
100.0%

cod_pais_nacim
Real number (ℝ)

MISSING 

Distinct33
Distinct (%)0.1%
Missing2441
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean221.0705918
Minimum16
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:20.110843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile169
Q1169
median169
Q3169
95-th percentile862
Maximum862
Range846
Interquartile range (IQR)0

Descriptive statistics

Standard deviation182.1262574
Coefficient of variation (CV)0.823837562
Kurtosis8.366438037
Mean221.0705918
Median Absolute Deviation (MAD)0
Skewness3.215642754
Sum5095235
Variance33169.97363
MonotonicityNot monotonic
2024-06-01T09:48:20.298302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
169 21246
83.4%
862 1617
 
6.3%
840 73
 
0.3%
850 15
 
0.1%
250 10
 
< 0.1%
218 10
 
< 0.1%
32 9
 
< 0.1%
124 8
 
< 0.1%
826 6
 
< 0.1%
724 5
 
< 0.1%
Other values (23) 49
 
0.2%
(Missing) 2441
 
9.6%
ValueCountFrequency (%)
16 1
 
< 0.1%
32 9
< 0.1%
68 3
 
< 0.1%
124 8
< 0.1%
152 2
 
< 0.1%
ValueCountFrequency (%)
862 1617
6.3%
858 2
 
< 0.1%
850 15
 
0.1%
840 73
 
0.3%
826 6
 
< 0.1%

cv
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing2441
Missing (%)9.6%
Memory size199.3 KiB
2024-06-01T09:48:20.438900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.928236723
Min length4

Characters and Unicode

Total characters113586
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMEDIO
2nd rowMEDIO
3rd rowMEDIO
4th rowMEDIO
5th rowMEDIO
ValueCountFrequency (%)
medio 21394
92.8%
alto 1650
 
7.2%
bajo 4
 
< 0.1%
2024-06-01T09:48:20.766908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 23048
20.3%
M 21394
18.8%
E 21394
18.8%
D 21394
18.8%
I 21394
18.8%
A 1654
 
1.5%
L 1650
 
1.5%
T 1650
 
1.5%
B 4
 
< 0.1%
J 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 113586
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 23048
20.3%
M 21394
18.8%
E 21394
18.8%
D 21394
18.8%
I 21394
18.8%
A 1654
 
1.5%
L 1650
 
1.5%
T 1650
 
1.5%
B 4
 
< 0.1%
J 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 113586
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 23048
20.3%
M 21394
18.8%
E 21394
18.8%
D 21394
18.8%
I 21394
18.8%
A 1654
 
1.5%
L 1650
 
1.5%
T 1650
 
1.5%
B 4
 
< 0.1%
J 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 23048
20.3%
M 21394
18.8%
E 21394
18.8%
D 21394
18.8%
I 21394
18.8%
A 1654
 
1.5%
L 1650
 
1.5%
T 1650
 
1.5%
B 4
 
< 0.1%
J 4
 
< 0.1%

pais_origen_recursos
Text

MISSING 

Distinct47
Distinct (%)0.2%
Missing4597
Missing (%)18.0%
Memory size199.3 KiB
2024-06-01T09:48:20.969987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters41784
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowCO
2nd rowCO
3rd rowCO
4th rowCO
5th rowCO
ValueCountFrequency (%)
co 19860
95.1%
us 539
 
2.6%
es 99
 
0.5%
ec 87
 
0.4%
cl 61
 
0.3%
ve 25
 
0.1%
ca 20
 
0.1%
mx 18
 
0.1%
pa 18
 
0.1%
pe 15
 
0.1%
Other values (37) 150
 
0.7%
2024-06-01T09:48:21.317931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 20060
48.0%
O 19866
47.5%
S 646
 
1.5%
U 548
 
1.3%
E 234
 
0.6%
L 65
 
0.2%
A 62
 
0.1%
P 45
 
0.1%
R 40
 
0.1%
V 29
 
0.1%
Other values (15) 189
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 41784
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 20060
48.0%
O 19866
47.5%
S 646
 
1.5%
U 548
 
1.3%
E 234
 
0.6%
L 65
 
0.2%
A 62
 
0.1%
P 45
 
0.1%
R 40
 
0.1%
V 29
 
0.1%
Other values (15) 189
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 41784
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 20060
48.0%
O 19866
47.5%
S 646
 
1.5%
U 548
 
1.3%
E 234
 
0.6%
L 65
 
0.2%
A 62
 
0.1%
P 45
 
0.1%
R 40
 
0.1%
V 29
 
0.1%
Other values (15) 189
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 20060
48.0%
O 19866
47.5%
S 646
 
1.5%
U 548
 
1.3%
E 234
 
0.6%
L 65
 
0.2%
A 62
 
0.1%
P 45
 
0.1%
R 40
 
0.1%
V 29
 
0.1%
Other values (15) 189
 
0.5%

pais_origen_recursos1
Text

MISSING 

Distinct47
Distinct (%)0.2%
Missing4597
Missing (%)18.0%
Memory size199.3 KiB
2024-06-01T09:48:21.567901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length36
Median length8
Mean length8.179781735
Min length4

Characters and Unicode

Total characters170892
Distinct characters31
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA
ValueCountFrequency (%)
colombia 19860
92.0%
unidos 541
 
2.5%
estados 539
 
2.5%
espaã‘a 99
 
0.5%
ecuador 87
 
0.4%
chile 61
 
0.3%
de 30
 
0.1%
venezuela 25
 
0.1%
repãšblica 25
 
0.1%
bolivariana 25
 
0.1%
Other values (57) 304
 
1.4%
2024-06-01T09:48:22.027260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 41032
24.0%
A 21202
12.4%
I 20717
12.1%
C 20130
11.8%
L 20065
11.7%
B 19922
11.7%
M 19920
11.7%
S 1774
 
1.0%
D 1252
 
0.7%
E 1051
 
0.6%
Other values (21) 3827
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 170037
99.5%
Space Separator 704
 
0.4%
Initial Punctuation 99
 
0.1%
Other Punctuation 25
 
< 0.1%
Lowercase Letter 25
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 41032
24.1%
A 21202
12.5%
I 20717
12.2%
C 20130
11.8%
L 20065
11.8%
B 19922
11.7%
M 19920
11.7%
S 1774
 
1.0%
D 1252
 
0.7%
E 1051
 
0.6%
Other values (15) 2972
 
1.7%
Space Separator
ValueCountFrequency (%)
704
100.0%
Initial Punctuation
ValueCountFrequency (%)
99
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%
Lowercase Letter
ValueCountFrequency (%)
š 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 170062
99.5%
Common 830
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 41032
24.1%
A 21202
12.5%
I 20717
12.2%
C 20130
11.8%
L 20065
11.8%
B 19922
11.7%
M 19920
11.7%
S 1774
 
1.0%
D 1252
 
0.7%
E 1051
 
0.6%
Other values (16) 2997
 
1.8%
Common
ValueCountFrequency (%)
704
84.8%
99
 
11.9%
, 25
 
3.0%
( 1
 
0.1%
) 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170644
99.9%
None 149
 
0.1%
Punctuation 99
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 41032
24.0%
A 21202
12.4%
I 20717
12.1%
C 20130
11.8%
L 20065
11.8%
B 19922
11.7%
M 19920
11.7%
S 1774
 
1.0%
D 1252
 
0.7%
E 1051
 
0.6%
Other values (18) 3579
 
2.1%
None
ValueCountFrequency (%)
à 124
83.2%
š 25
 
16.8%
Punctuation
ValueCountFrequency (%)
99
100.0%
Distinct3
Distinct (%)< 0.1%
Missing4597
Missing (%)18.0%
Memory size199.3 KiB
2024-06-01T09:48:22.199095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.995069883
Min length4

Characters and Unicode

Total characters104357
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMEDIO
2nd rowMEDIO
3rd rowMEDIO
4th rowMEDIO
5th rowMEDIO
ValueCountFrequency (%)
medio 20789
99.5%
alto 69
 
0.3%
bajo 34
 
0.2%
2024-06-01T09:48:22.542795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 20892
20.0%
M 20789
19.9%
E 20789
19.9%
D 20789
19.9%
I 20789
19.9%
A 103
 
0.1%
L 69
 
0.1%
T 69
 
0.1%
B 34
 
< 0.1%
J 34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 104357
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 20892
20.0%
M 20789
19.9%
E 20789
19.9%
D 20789
19.9%
I 20789
19.9%
A 103
 
0.1%
L 69
 
0.1%
T 69
 
0.1%
B 34
 
< 0.1%
J 34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 104357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 20892
20.0%
M 20789
19.9%
E 20789
19.9%
D 20789
19.9%
I 20789
19.9%
A 103
 
0.1%
L 69
 
0.1%
T 69
 
0.1%
B 34
 
< 0.1%
J 34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 20892
20.0%
M 20789
19.9%
E 20789
19.9%
D 20789
19.9%
I 20789
19.9%
A 103
 
0.1%
L 69
 
0.1%
T 69
 
0.1%
B 34
 
< 0.1%
J 34
 
< 0.1%

pais_residencia
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)0.1%
Missing922
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean171.5378353
Minimum36
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:22.714597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile169
Q1169
median169
Q3169
95-th percentile169
Maximum862
Range826
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.04325014
Coefficient of variation (CV)0.2334368395
Kurtosis249.319398
Mean171.5378353
Median Absolute Deviation (MAD)0
Skewness15.73450953
Sum4214170
Variance1603.461882
MonotonicityNot monotonic
2024-06-01T09:48:22.886429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
169 24443
95.9%
840 57
 
0.2%
724 26
 
0.1%
124 5
 
< 0.1%
826 5
 
< 0.1%
484 3
 
< 0.1%
152 3
 
< 0.1%
250 3
 
< 0.1%
862 3
 
< 0.1%
36 3
 
< 0.1%
Other values (10) 16
 
0.1%
(Missing) 922
 
3.6%
ValueCountFrequency (%)
36 3
 
< 0.1%
40 2
 
< 0.1%
124 5
 
< 0.1%
152 3
 
< 0.1%
169 24443
95.9%
ValueCountFrequency (%)
862 3
 
< 0.1%
840 57
0.2%
826 5
 
< 0.1%
784 1
 
< 0.1%
756 2
 
< 0.1%

pais_residencia1
Text

MISSING 

Distinct20
Distinct (%)0.1%
Missing922
Missing (%)3.6%
Memory size199.3 KiB
2024-06-01T09:48:23.107800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length15
Median length8
Mean length8.011926568
Min length4

Characters and Unicode

Total characters196829
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA
ValueCountFrequency (%)
colombia 24443
99.2%
unidos 57
 
0.2%
estados 57
 
0.2%
españa 26
 
0.1%
canada 5
 
< 0.1%
reino 5
 
< 0.1%
unido 5
 
< 0.1%
venezuela 3
 
< 0.1%
rep 3
 
< 0.1%
australia 3
 
< 0.1%
Other values (16) 29
 
0.1%
2024-06-01T09:48:23.513921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 49020
24.9%
A 24613
12.5%
I 24540
12.5%
C 24464
12.4%
L 24456
12.4%
M 24451
12.4%
B 24444
12.4%
S 207
 
0.1%
D 128
 
0.1%
E 109
 
0.1%
Other values (13) 397
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 196760
> 99.9%
Space Separator 69
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 49020
24.9%
A 24613
12.5%
I 24540
12.5%
C 24464
12.4%
L 24456
12.4%
M 24451
12.4%
B 24444
12.4%
S 207
 
0.1%
D 128
 
0.1%
E 109
 
0.1%
Other values (12) 328
 
0.2%
Space Separator
ValueCountFrequency (%)
69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 196760
> 99.9%
Common 69
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 49020
24.9%
A 24613
12.5%
I 24540
12.5%
C 24464
12.4%
L 24456
12.4%
M 24451
12.4%
B 24444
12.4%
S 207
 
0.1%
D 128
 
0.1%
E 109
 
0.1%
Other values (12) 328
 
0.2%
Common
ValueCountFrequency (%)
69
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196803
> 99.9%
None 26
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 49020
24.9%
A 24613
12.5%
I 24540
12.5%
C 24464
12.4%
L 24456
12.4%
M 24451
12.4%
B 24444
12.4%
S 207
 
0.1%
D 128
 
0.1%
E 109
 
0.1%
Other values (12) 371
 
0.2%
None
ValueCountFrequency (%)
Ñ 26
100.0%
Distinct3
Distinct (%)< 0.1%
Missing922
Missing (%)3.6%
Memory size199.3 KiB
2024-06-01T09:48:23.685760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.99934872
Min length4

Characters and Unicode

Total characters122819
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMEDIO
2nd rowMEDIO
3rd rowMEDIO
4th rowMEDIO
5th rowMEDIO
ValueCountFrequency (%)
medio 24551
99.9%
bajo 9
 
< 0.1%
alto 7
 
< 0.1%
2024-06-01T09:48:24.045451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 24567
20.0%
M 24551
20.0%
E 24551
20.0%
D 24551
20.0%
I 24551
20.0%
A 16
 
< 0.1%
B 9
 
< 0.1%
J 9
 
< 0.1%
L 7
 
< 0.1%
T 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 122819
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 24567
20.0%
M 24551
20.0%
E 24551
20.0%
D 24551
20.0%
I 24551
20.0%
A 16
 
< 0.1%
B 9
 
< 0.1%
J 9
 
< 0.1%
L 7
 
< 0.1%
T 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 122819
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 24567
20.0%
M 24551
20.0%
E 24551
20.0%
D 24551
20.0%
I 24551
20.0%
A 16
 
< 0.1%
B 9
 
< 0.1%
J 9
 
< 0.1%
L 7
 
< 0.1%
T 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 24567
20.0%
M 24551
20.0%
E 24551
20.0%
D 24551
20.0%
I 24551
20.0%
A 16
 
< 0.1%
B 9
 
< 0.1%
J 9
 
< 0.1%
L 7
 
< 0.1%
T 7
 
< 0.1%

f_vinc
Real number (ℝ)

MISSING 

Distinct5588
Distinct (%)22.6%
Missing748
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean20161468.29
Minimum19111111
Maximum20240312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:24.248559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum19111111
5-th percentile19981029
Q120140313
median20190426
Q320211216
95-th percentile20230327
Maximum20240312
Range1129201
Interquartile range (IQR)70903

Descriptive statistics

Standard deviation80105.52499
Coefficient of variation (CV)0.003973198968
Kurtosis15.79518588
Mean20161468.29
Median Absolute Deviation (MAD)29998
Skewness-2.630063729
Sum4.988148869 × 1011
Variance6416895135
MonotonicityNot monotonic
2024-06-01T09:48:24.467254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180723 211
 
0.8%
20170829 169
 
0.7%
20220511 89
 
0.3%
20220512 78
 
0.3%
20220108 75
 
0.3%
20220429 61
 
0.2%
20210611 60
 
0.2%
20220310 58
 
0.2%
20220301 57
 
0.2%
20180731 54
 
0.2%
Other values (5578) 23829
93.5%
(Missing) 748
 
2.9%
ValueCountFrequency (%)
19111111 11
< 0.1%
19450101 1
 
< 0.1%
19740401 12
< 0.1%
19750401 2
 
< 0.1%
19770725 1
 
< 0.1%
ValueCountFrequency (%)
20240312 2
< 0.1%
20240311 2
< 0.1%
20240309 1
< 0.1%
20240307 2
< 0.1%
20240306 1
< 0.1%

estado_cli
Text

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing818
Missing (%)3.2%
Memory size199.3 KiB
2024-06-01T09:48:24.670305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length19
Median length6
Mean length6.904178996
Min length6

Characters and Unicode

Total characters170333
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACTIVO
2nd rowACTIVO
3rd rowINACTIVO
4th rowINACTIVO
5th rowACTIVO
ValueCountFrequency (%)
activo 13806
55.7%
inactivo 10808
43.6%
ley 52
 
0.2%
de 52
 
0.2%
intervención 52
 
0.2%
fallecido 5
 
< 0.1%
2024-06-01T09:48:25.357642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 35531
20.9%
C 24671
14.5%
T 24666
14.5%
V 24666
14.5%
A 24619
14.5%
O 24619
14.5%
N 10964
 
6.4%
E 213
 
0.1%
104
 
0.1%
L 62
 
< 0.1%
Other values (5) 218
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 170229
99.9%
Space Separator 104
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 35531
20.9%
C 24671
14.5%
T 24666
14.5%
V 24666
14.5%
A 24619
14.5%
O 24619
14.5%
N 10964
 
6.4%
E 213
 
0.1%
L 62
 
< 0.1%
D 57
 
< 0.1%
Other values (4) 161
 
0.1%
Space Separator
ValueCountFrequency (%)
104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 170229
99.9%
Common 104
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 35531
20.9%
C 24671
14.5%
T 24666
14.5%
V 24666
14.5%
A 24619
14.5%
O 24619
14.5%
N 10964
 
6.4%
E 213
 
0.1%
L 62
 
< 0.1%
D 57
 
< 0.1%
Other values (4) 161
 
0.1%
Common
ValueCountFrequency (%)
104
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170281
> 99.9%
None 52
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 35531
20.9%
C 24671
14.5%
T 24666
14.5%
V 24666
14.5%
A 24619
14.5%
O 24619
14.5%
N 10964
 
6.4%
E 213
 
0.1%
104
 
0.1%
L 62
 
< 0.1%
Other values (4) 166
 
0.1%
None
ValueCountFrequency (%)
Ó 52
100.0%

ctrl_terc
Text

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing748
Missing (%)2.9%
Memory size199.3 KiB
2024-06-01T09:48:25.545127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length19
Median length7
Mean length7.959338749
Min length5

Characters and Unicode

Total characters196922
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLIENTE
2nd rowCLIENTE
3rd rowEXCLIENTE
4th rowEXCLIENTE
5th rowCLIENTE
ValueCountFrequency (%)
cliente 15682
60.7%
excliente 7974
30.9%
social 1087
 
4.2%
prospecto 519
 
2.0%
nequi 487
 
1.9%
conyuge 69
 
0.3%
representante 8
 
< 0.1%
legal 8
 
< 0.1%
usuarios 2
 
< 0.1%
deceval 2
 
< 0.1%
2024-06-01T09:48:25.959219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 56405
28.6%
C 25333
12.9%
I 25232
12.8%
L 24761
12.6%
N 24228
12.3%
T 24191
12.3%
X 7974
 
4.0%
O 2196
 
1.1%
S 1618
 
0.8%
A 1107
 
0.6%
Other values (9) 3877
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 195825
99.4%
Space Separator 1097
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 56405
28.8%
C 25333
12.9%
I 25232
12.9%
L 24761
12.6%
N 24228
12.4%
T 24191
12.4%
X 7974
 
4.1%
O 2196
 
1.1%
S 1618
 
0.8%
A 1107
 
0.6%
Other values (8) 2780
 
1.4%
Space Separator
ValueCountFrequency (%)
1097
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 195825
99.4%
Common 1097
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 56405
28.8%
C 25333
12.9%
I 25232
12.9%
L 24761
12.6%
N 24228
12.4%
T 24191
12.4%
X 7974
 
4.1%
O 2196
 
1.1%
S 1618
 
0.8%
A 1107
 
0.6%
Other values (8) 2780
 
1.4%
Common
ValueCountFrequency (%)
1097
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 56405
28.6%
C 25333
12.9%
I 25232
12.8%
L 24761
12.6%
N 24228
12.3%
T 24191
12.3%
X 7974
 
4.0%
O 2196
 
1.1%
S 1618
 
0.8%
A 1107
 
0.6%
Other values (9) 3877
 
2.0%

riesgo_cliente__ric_
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing5159
Missing (%)20.2%
Memory size199.3 KiB
2024-06-01T09:48:26.148123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.45046729
Min length4

Characters and Unicode

Total characters90478
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALTO
2nd rowALTO
3rd rowBAJO
4th rowBAJO
5th rowALTO
ValueCountFrequency (%)
medio 9158
45.0%
alto 5849
28.8%
bajo 5323
26.2%
2024-06-01T09:48:26.507826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 20330
22.5%
A 11172
12.3%
M 9158
10.1%
E 9158
10.1%
D 9158
10.1%
I 9158
10.1%
L 5849
 
6.5%
T 5849
 
6.5%
B 5323
 
5.9%
J 5323
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 90478
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 20330
22.5%
A 11172
12.3%
M 9158
10.1%
E 9158
10.1%
D 9158
10.1%
I 9158
10.1%
L 5849
 
6.5%
T 5849
 
6.5%
B 5323
 
5.9%
J 5323
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 90478
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 20330
22.5%
A 11172
12.3%
M 9158
10.1%
E 9158
10.1%
D 9158
10.1%
I 9158
10.1%
L 5849
 
6.5%
T 5849
 
6.5%
B 5323
 
5.9%
J 5323
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 20330
22.5%
A 11172
12.3%
M 9158
10.1%
E 9158
10.1%
D 9158
10.1%
I 9158
10.1%
L 5849
 
6.5%
T 5849
 
6.5%
B 5323
 
5.9%
J 5323
 
5.9%

f_ingreso_lc
Real number (ℝ)

MISSING 

Distinct1237
Distinct (%)18.3%
Missing18730
Missing (%)73.5%
Infinite0
Infinite (%)0.0%
Mean20163440.12
Minimum20010131
Maximum20240304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:26.726488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum20010131
5-th percentile20070204.5
Q120121119
median20180924
Q320211025
95-th percentile20230626
Maximum20240304
Range230173
Interquartile range (IQR)89906

Descriptive statistics

Standard deviation56763.83241
Coefficient of variation (CV)0.002815185905
Kurtosis-0.4112938563
Mean20163440.12
Median Absolute Deviation (MAD)30612
Skewness-0.7892117953
Sum1.362846917 × 1011
Variance3222132669
MonotonicityNot monotonic
2024-06-01T09:48:26.945218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180924 443
 
1.7%
20080123 441
 
1.7%
20211201 243
 
1.0%
20121129 164
 
0.6%
20211103 148
 
0.6%
20190130 118
 
0.5%
20020314 99
 
0.4%
20090907 72
 
0.3%
20230816 63
 
0.2%
20191023 62
 
0.2%
Other values (1227) 4906
 
19.2%
(Missing) 18730
73.5%
ValueCountFrequency (%)
20010131 42
0.2%
20010509 1
 
< 0.1%
20010801 2
 
< 0.1%
20020313 2
 
< 0.1%
20020314 99
0.4%
ValueCountFrequency (%)
20240304 1
 
< 0.1%
20240229 2
< 0.1%
20240228 1
 
< 0.1%
20240226 2
< 0.1%
20240223 3
< 0.1%

cod_categ_lc
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)0.1%
Missing18730
Missing (%)73.5%
Infinite0
Infinite (%)0.0%
Mean9.093061104
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:27.117050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median5
Q39
95-th percentile38
Maximum49
Range48
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.989165961
Coefficient of variation (CV)1.098548206
Kurtosis7.296779859
Mean9.093061104
Median Absolute Deviation (MAD)3
Skewness2.750076557
Sum61460
Variance99.7834366
MonotonicityNot monotonic
2024-06-01T09:48:27.273234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 2849
 
11.2%
9 2048
 
8.0%
2 1062
 
4.2%
25 447
 
1.8%
49 191
 
0.7%
38 104
 
0.4%
48 45
 
0.2%
1 13
 
0.1%
(Missing) 18730
73.5%
ValueCountFrequency (%)
1 13
 
0.1%
2 1062
 
4.2%
5 2849
11.2%
9 2048
8.0%
25 447
 
1.8%
ValueCountFrequency (%)
49 191
 
0.7%
48 45
 
0.2%
38 104
 
0.4%
25 447
 
1.8%
9 2048
8.0%

desc_categ
Text

MISSING 

Distinct8
Distinct (%)0.1%
Missing18730
Missing (%)73.5%
Memory size199.3 KiB
2024-06-01T09:48:27.507585image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length50
Median length50
Mean length50
Min length50

Characters and Unicode

Total characters337950
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANTES DE ATENDER REALICE GESTION ADICIONAL
2nd rowANTES DE ATENDER REALICE GESTION ADICIONAL
3rd rowANTES DE ATENDER REALICE GESTION ADICIONAL
4th rowANTES DE ATENDER REALICE GESTION ADICIONAL
5th rowBLOQUEORESTRICTIVO HACER GESTION ADICIONAL CLIENTE
ValueCountFrequency (%)
gestion 6004
16.1%
adicional 3911
10.5%
bloqueorestrictivo 3110
8.3%
hacer 3110
8.3%
cliente 3110
8.3%
de 2894
7.8%
antes 2849
7.6%
atender 2849
7.6%
realice 2849
7.6%
adicio 2048
 
5.5%
Other values (19) 4517
12.1%
2024-06-01T09:48:27.928783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
68255
20.2%
E 40832
12.1%
I 32436
9.6%
O 27139
 
8.0%
A 22855
 
6.8%
T 22691
 
6.7%
N 21817
 
6.5%
C 19679
 
5.8%
R 17470
 
5.2%
L 13806
 
4.1%
Other values (14) 50970
15.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 269533
79.8%
Space Separator 68255
 
20.2%
Dash Punctuation 162
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 40832
15.1%
I 32436
12.0%
O 27139
10.1%
A 22855
8.5%
T 22691
8.4%
N 21817
8.1%
C 19679
7.3%
R 17470
6.5%
L 13806
 
5.1%
S 13078
 
4.9%
Other values (12) 37730
14.0%
Space Separator
ValueCountFrequency (%)
68255
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 269533
79.8%
Common 68417
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 40832
15.1%
I 32436
12.0%
O 27139
10.1%
A 22855
8.5%
T 22691
8.4%
N 21817
8.1%
C 19679
7.3%
R 17470
6.5%
L 13806
 
5.1%
S 13078
 
4.9%
Other values (12) 37730
14.0%
Common
ValueCountFrequency (%)
68255
99.8%
- 162
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 337950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
68255
20.2%
E 40832
12.1%
I 32436
9.6%
O 27139
 
8.0%
A 22855
 
6.8%
T 22691
 
6.7%
N 21817
 
6.5%
C 19679
 
5.8%
R 17470
 
5.2%
L 13806
 
4.1%
Other values (14) 50970
15.1%

cod_subcateg_lc
Real number (ℝ)

MISSING 

Distinct33
Distinct (%)0.5%
Missing18730
Missing (%)73.5%
Infinite0
Infinite (%)0.0%
Mean18.11880456
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:28.225589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median26
Q328
95-th percentile31
Maximum46
Range45
Interquartile range (IQR)25

Descriptive statistics

Standard deviation12.6509448
Coefficient of variation (CV)0.6982218257
Kurtosis-1.68017673
Mean18.11880456
Median Absolute Deviation (MAD)5
Skewness-0.3030416743
Sum122465
Variance160.0464043
MonotonicityNot monotonic
2024-06-01T09:48:28.506773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
3 1403
 
5.5%
27 1147
 
4.5%
28 1028
 
4.0%
26 728
 
2.9%
31 647
 
2.5%
1 509
 
2.0%
2 430
 
1.7%
8 151
 
0.6%
25 132
 
0.5%
32 109
 
0.4%
Other values (23) 475
 
1.9%
(Missing) 18730
73.5%
ValueCountFrequency (%)
1 509
 
2.0%
2 430
 
1.7%
3 1403
5.5%
4 1
 
< 0.1%
5 23
 
0.1%
ValueCountFrequency (%)
46 1
 
< 0.1%
43 5
 
< 0.1%
42 7
 
< 0.1%
41 20
0.1%
40 37
0.1%

desc_subcateg
Text

MISSING 

Distinct35
Distinct (%)0.5%
Missing18730
Missing (%)73.5%
Memory size199.3 KiB
2024-06-01T09:48:28.881684image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length50
Median length50
Mean length50
Min length50

Characters and Unicode

Total characters337950
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMEDIOS DE COMUNICACION
2nd rowMEDIOS DE COMUNICACION
3rd rowRESULTADO ANALISIS INTERNO
4th rowINVESTIGADOS OFAC
5th rowCLIENTE CON REQUERIMIENTOS
ValueCountFrequency (%)
requerimientos 2130
13.0%
resultado 1911
11.6%
analisis 1911
11.6%
interno 1911
11.6%
cliente 996
 
6.1%
de 975
 
5.9%
medios 813
 
4.9%
comunicacion 813
 
4.9%
con 699
 
4.3%
desmovilizados 646
 
3.9%
Other values (56) 3636
22.1%
2024-06-01T09:48:29.522159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
199665
59.1%
I 17849
 
5.3%
E 17405
 
5.2%
O 14602
 
4.3%
N 13147
 
3.9%
S 11841
 
3.5%
R 10259
 
3.0%
A 9280
 
2.7%
T 8116
 
2.4%
C 6528
 
1.9%
Other values (17) 29258
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 199665
59.1%
Uppercase Letter 138188
40.9%
Dash Punctuation 80
 
< 0.1%
Other Punctuation 17
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 17849
12.9%
E 17405
12.6%
O 14602
10.6%
N 13147
9.5%
S 11841
8.6%
R 10259
7.4%
A 9280
6.7%
T 8116
 
5.9%
C 6528
 
4.7%
D 6495
 
4.7%
Other values (14) 22666
16.4%
Space Separator
ValueCountFrequency (%)
199665
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%
Other Punctuation
ValueCountFrequency (%)
# 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 199762
59.1%
Latin 138188
40.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 17849
12.9%
E 17405
12.6%
O 14602
10.6%
N 13147
9.5%
S 11841
8.6%
R 10259
7.4%
A 9280
6.7%
T 8116
 
5.9%
C 6528
 
4.7%
D 6495
 
4.7%
Other values (14) 22666
16.4%
Common
ValueCountFrequency (%)
199665
> 99.9%
- 80
 
< 0.1%
# 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 337950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
199665
59.1%
I 17849
 
5.3%
E 17405
 
5.2%
O 14602
 
4.3%
N 13147
 
3.9%
S 11841
 
3.5%
R 10259
 
3.0%
A 9280
 
2.7%
T 8116
 
2.4%
C 6528
 
1.9%
Other values (17) 29258
 
8.7%

cod_nivel_cat
Real number (ℝ)

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing18730
Missing (%)73.5%
Infinite0
Infinite (%)0.0%
Mean2.528184643
Minimum2
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:29.818964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q33
95-th percentile3
Maximum3
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4992419269
Coefficient of variation (CV)0.1974705164
Kurtosis-1.987832129
Mean2.528184643
Median Absolute Deviation (MAD)0
Skewness-0.1129431786
Sum17088
Variance0.2492425016
MonotonicityNot monotonic
2024-06-01T09:48:30.053282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
3 3570
 
14.0%
2 3189
 
12.5%
(Missing) 18730
73.5%
ValueCountFrequency (%)
2 3189
12.5%
3 3570
14.0%
ValueCountFrequency (%)
3 3570
14.0%
2 3189
12.5%
Distinct2529
Distinct (%)37.4%
Missing18730
Missing (%)73.5%
Memory size199.3 KiB
2024-06-01T09:48:30.381980image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length154
Median length150
Mean length150.0016275
Min length41

Characters and Unicode

Total characters1013861
Distinct characters94
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1931 ?
Unique (%)28.6%

Sample

1st rowDE BLOQUEO A ALERTA-REUBICADO-FECHAINGLC:20150316-VIENE:CAT:05-SUBCAT:05-NO USAR-ILICITO/FRAUDE
2nd rowDE BLOQUEO A ALERTA-RECLASIFICACION CAT.05 PRESUNTA CONDUCTA: TERRORISMO
3rd rowCLIENTE CON OPERACIONES INTERNACIONALES REPORTADOS SEGÚN REQUERIMIENTO DEL JP MORGAN
4th rowCLIENTE EN INVESTIGACION POR AUTORIDAD INTERNACIONAL OFAC *CONFIDENCIAL* CASO GESTOR 206850
5th rowof:FEO113-rub:RL01192232--2024-02-21 05:00:00-UIAF
ValueCountFrequency (%)
de 3521
 
8.2%
1930
 
4.5%
caso 955
 
2.2%
cat 845
 
2.0%
desmovilizados 824
 
1.9%
del 765
 
1.8%
nacional 759
 
1.8%
se 699
 
1.6%
alerta 690
 
1.6%
decision 685
 
1.6%
Other values (3960) 31436
72.9%
2024-06-01T09:48:30.891718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
625949
61.7%
A 32034
 
3.2%
O 28427
 
2.8%
E 28194
 
2.8%
I 26276
 
2.6%
C 22677
 
2.2%
R 20773
 
2.0%
0 19969
 
2.0%
N 17503
 
1.7%
- 14994
 
1.5%
Other values (84) 177065
 
17.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 625950
61.7%
Uppercase Letter 271631
26.8%
Decimal Number 67662
 
6.7%
Lowercase Letter 24277
 
2.4%
Dash Punctuation 14994
 
1.5%
Other Punctuation 9083
 
0.9%
Close Punctuation 84
 
< 0.1%
Open Punctuation 83
 
< 0.1%
Connector Punctuation 62
 
< 0.1%
Other Symbol 31
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 32034
11.8%
O 28427
10.5%
E 28194
10.4%
I 26276
9.7%
C 22677
8.3%
R 20773
 
7.6%
N 17503
 
6.4%
T 14837
 
5.5%
S 14302
 
5.3%
L 13260
 
4.9%
Other values (22) 53348
19.6%
Lowercase Letter
ValueCountFrequency (%)
o 4292
17.7%
a 2744
11.3%
i 2425
10.0%
s 1884
7.8%
e 1636
 
6.7%
d 1550
 
6.4%
c 1448
 
6.0%
r 1425
 
5.9%
t 1147
 
4.7%
l 1086
 
4.5%
Other values (19) 4640
19.1%
Other Punctuation
ValueCountFrequency (%)
: 6884
75.8%
. 1106
 
12.2%
/ 698
 
7.7%
" 185
 
2.0%
, 153
 
1.7%
* 32
 
0.4%
# 9
 
0.1%
% 6
 
0.1%
& 4
 
< 0.1%
? 3
 
< 0.1%
Other values (2) 3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 19969
29.5%
2 14192
21.0%
1 10368
15.3%
3 4578
 
6.8%
9 3935
 
5.8%
4 3819
 
5.6%
7 3231
 
4.8%
5 2761
 
4.1%
8 2529
 
3.7%
6 2280
 
3.4%
Space Separator
ValueCountFrequency (%)
625949
> 99.9%
  1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
¦ 29
93.5%
° 2
 
6.5%
Math Symbol
ValueCountFrequency (%)
| 1
50.0%
+ 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 14994
100.0%
Close Punctuation
ValueCountFrequency (%)
) 84
100.0%
Open Punctuation
ValueCountFrequency (%)
( 83
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 62
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 717953
70.8%
Latin 295908
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 32034
10.8%
O 28427
 
9.6%
E 28194
 
9.5%
I 26276
 
8.9%
C 22677
 
7.7%
R 20773
 
7.0%
N 17503
 
5.9%
T 14837
 
5.0%
S 14302
 
4.8%
L 13260
 
4.5%
Other values (51) 77625
26.2%
Common
ValueCountFrequency (%)
625949
87.2%
0 19969
 
2.8%
- 14994
 
2.1%
2 14192
 
2.0%
1 10368
 
1.4%
: 6884
 
1.0%
3 4578
 
0.6%
9 3935
 
0.5%
4 3819
 
0.5%
7 3231
 
0.5%
Other values (23) 10034
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1013197
99.9%
None 664
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
625949
61.8%
A 32034
 
3.2%
O 28427
 
2.8%
E 28194
 
2.8%
I 26276
 
2.6%
C 22677
 
2.2%
R 20773
 
2.1%
0 19969
 
2.0%
N 17503
 
1.7%
- 14994
 
1.5%
Other values (70) 176401
 
17.4%
None
ValueCountFrequency (%)
Ó 507
76.4%
Í 56
 
8.4%
¦ 29
 
4.4%
Á 22
 
3.3%
í 15
 
2.3%
ó 12
 
1.8%
Ñ 10
 
1.5%
É 4
 
0.6%
° 2
 
0.3%
Ú 2
 
0.3%
Other values (4) 5
 
0.8%

monto_total_anual_transado__efectivo_
Real number (ℝ)

SKEWED  ZEROS 

Distinct8133
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean768648370.6
Minimum0
Maximum2.171737125 × 1012
Zeros12710
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:31.110423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median90000
Q340511849.75
95-th percentile595239661
Maximum2.171737125 × 1012
Range2.171737125 × 1012
Interquartile range (IQR)40511849.75

Descriptive statistics

Standard deviation1.586218721 × 1010
Coefficient of variation (CV)20.63646762
Kurtosis13939.54466
Mean768648370.6
Median Absolute Deviation (MAD)90000
Skewness105.6728519
Sum1.959207832 × 1013
Variance2.516089832 × 1020
MonotonicityNot monotonic
2024-06-01T09:48:31.329121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12710
49.9%
89760000 32
 
0.1%
979000000 24
 
0.1%
21830000 24
 
0.1%
100000 24
 
0.1%
500000 20
 
0.1%
2620000 20
 
0.1%
11400000 18
 
0.1%
1.418688606 × 101117
 
0.1%
402351000 17
 
0.1%
Other values (8123) 12583
49.4%
ValueCountFrequency (%)
0 12710
49.9%
8000 1
 
< 0.1%
10000 2
 
< 0.1%
20000 5
 
< 0.1%
25000 1
 
< 0.1%
ValueCountFrequency (%)
2.171737125 × 10121
< 0.1%
7.070388413 × 10111
< 0.1%
2.868074181 × 10112
< 0.1%
2.29663416 × 10111
< 0.1%
1.958564266 × 10111
< 0.1%

frecuencia_total_anual_transada__efectivo_
Real number (ℝ)

SKEWED  ZEROS 

Distinct473
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1839.82361
Minimum0
Maximum42940000
Zeros12710
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:31.532225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q347
95-th percentile176
Maximum42940000
Range42940000
Interquartile range (IQR)47

Descriptive statistics

Standard deviation269356.0923
Coefficient of variation (CV)146.4032154
Kurtosis25338.7435
Mean1839.82361
Median Absolute Deviation (MAD)1
Skewness158.969723
Sum46895264
Variance7.255270444 × 1010
MonotonicityNot monotonic
2024-06-01T09:48:31.750930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12710
49.9%
1 409
 
1.6%
3 296
 
1.2%
2 272
 
1.1%
5 230
 
0.9%
4 191
 
0.7%
9 188
 
0.7%
6 174
 
0.7%
12 172
 
0.7%
21 166
 
0.7%
Other values (463) 10681
41.9%
ValueCountFrequency (%)
0 12710
49.9%
1 409
 
1.6%
2 272
 
1.1%
3 296
 
1.2%
4 191
 
0.7%
ValueCountFrequency (%)
42940000 1
< 0.1%
2289396 1
< 0.1%
464467 1
< 0.1%
82155 1
< 0.1%
26191 2
< 0.1%
Distinct5837
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135313004.9
Minimum0
Maximum2.281215564 × 1011
Zeros17640
Zeros (%)69.2%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:31.961200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326618428.56
95-th percentile207097949.5
Maximum2.281215564 × 1011
Range2.281215564 × 1011
Interquartile range (IQR)26618428.56

Descriptive statistics

Standard deviation3186508505
Coefficient of variation (CV)23.54916666
Kurtosis3492.77735
Mean135313004.9
Median Absolute Deviation (MAD)0
Skewness55.64027059
Sum3.448993183 × 1012
Variance1.015383645 × 1019
MonotonicityNot monotonic
2024-06-01T09:48:32.164280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17640
69.2%
5888645.19 11
 
< 0.1%
9674354.738 11
 
< 0.1%
100506912.1 10
 
< 0.1%
150362770.1 9
 
< 0.1%
17328552.97 9
 
< 0.1%
159085074 8
 
< 0.1%
75258344.33 8
 
< 0.1%
225657163.1 8
 
< 0.1%
18359934.83 8
 
< 0.1%
Other values (5827) 7767
30.5%
ValueCountFrequency (%)
0 17640
69.2%
17 1
 
< 0.1%
4458.87 1
 
< 0.1%
13570.92 1
 
< 0.1%
14007 1
 
< 0.1%
ValueCountFrequency (%)
2.281215564 × 10111
< 0.1%
2.196788473 × 10111
< 0.1%
2.149324003 × 10111
< 0.1%
1.828644038 × 10111
< 0.1%
1.618326992 × 10111
< 0.1%
Distinct401
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13694.11535
Minimum0
Maximum348400543.1
Zeros17640
Zeros (%)69.2%
Negative0
Negative (%)0.0%
Memory size199.3 KiB
2024-06-01T09:48:32.367360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile158
Maximum348400543.1
Range348400543.1
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2182239.403
Coefficient of variation (CV)159.3559969
Kurtosis25488.99994
Mean13694.11535
Median Absolute Deviation (MAD)0
Skewness159.6527479
Sum349049306.1
Variance4.76216881 × 1012
MonotonicityNot monotonic
2024-06-01T09:48:32.586053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17640
69.2%
1 668
 
2.6%
2 463
 
1.8%
3 273
 
1.1%
4 211
 
0.8%
5 148
 
0.6%
6 139
 
0.5%
8 121
 
0.5%
7 112
 
0.4%
9 108
 
0.4%
Other values (391) 5606
 
22.0%
ValueCountFrequency (%)
0 17640
69.2%
1 668
 
2.6%
2 463
 
1.8%
3 273
 
1.1%
4 211
 
0.8%
ValueCountFrequency (%)
348400543.1 1
 
< 0.1%
3218 3
< 0.1%
935 1
 
< 0.1%
921 3
< 0.1%
902 1
 
< 0.1%